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77 lines
3.3 KiB
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77 lines
3.3 KiB
ReStructuredText
.. image:: https://s3.amazonaws.com/enigmaco-docs/enigma-catalyst.png
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:target: https://enigmampc.github.io/catalyst
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:align: center
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:alt: Enigma | Catalyst
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Catalyst is an algorithmic trading library for crypto-assets written in Python.
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It allows trading strategies to be easily expressed and backtested against
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historical data (with daily and minute resolution), providing analytics and
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insights regarding a particular strategy's performance. Catalyst also supports
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live-trading of crypto-assets starting with three exchanges (Bitfinex, Bittrex,
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and Poloniex) with more being added over time. Catalyst empowers users to share
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and curate data and build profitable, data-driven investment strategies. Please
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visit `enigma.co <https://www.enigma.co>`_ to learn more about Catalyst.
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Catalyst builds on top of the well-established
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`Zipline <https://github.com/quantopian/zipline>`_ project. We did our best to
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minimize structural changes to the general API to maximize compatibility with
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existing trading algorithms, developer knowledge, and tutorials. Join us on the
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`Catalyst Forum <https://catalyst.enigma.co/>`_ for questions around Catalyst,
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algorithmic trading and technical support. We also have a
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`Discord <https://discord.gg/SJK32GY>`_ group with the *#catalyst_dev* and
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*#catalyst_setup* dedicated channels.
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Overview
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========
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- Ease of use: Catalyst tries to get out of your way so that you can
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focus on algorithm development. See
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`examples of trading strategies <https://github.com/enigmampc/catalyst/tree/master/catalyst/examples>`_
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provided.
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- Support for several of the top crypto-exchanges by trading volume:
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`Bitfinex <https://www.bitfinex.com>`_, `Bittrex <http://www.bittrex.com>`_,
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and `Poloniex <https://www.poloniex.com>`_.
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- Secure: You and only you have access to each exchange API keys for your accounts.
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- Input of historical pricing data of all crypto-assets by exchange,
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with daily and minute resolution. See
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`Catalyst Market Coverage Overview <https://www.enigma.co/catalyst/status>`_.
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- Backtesting and live-trading functionality, with a seamless transition
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between the two modes.
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- Output of performance statistics are based on Pandas DataFrames to
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integrate nicely into the existing PyData eco-system.
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- Statistic and machine learning libraries like matplotlib, scipy,
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statsmodels, and sklearn support development, analysis, and
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visualization of state-of-the-art trading systems.
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- Addition of Bitcoin price (btc_usdt) as a benchmark for comparing
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performance across trading algorithms.
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Go to our `Documentation Website <https://enigmampc.github.io/catalyst/>`_.
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.. |version tag| image:: https://img.shields.io/pypi/v/enigma-catalyst.svg
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:target: https://pypi.python.org/pypi/enigma-catalyst
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.. |version status| image:: https://img.shields.io/pypi/pyversions/enigma-catalyst.svg
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:target: https://pypi.python.org/pypi/enigma-catalyst
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.. |forum| image:: https://img.shields.io/badge/forum-join-green.svg
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:target: https://catalyst.enigma.co/
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.. |discord| image:: https://img.shields.io/badge/discord-join%20chat-green.svg
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:target: https://discordapp.com/invite/SJK32GY
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.. |twitter| image:: https://img.shields.io/twitter/follow/enigmampc.svg?style=social&label=Follow&style=flat-square
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:target: https://twitter.com/enigmampc
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